Machine Perception

Research in machine perception tackles the hard problems of understanding images,
sounds, music and video. In recent years, our computers have become much better at
such tasks, enabling a variety of new applications such as: content-based search in
Google Photos and Image Search, natural handwriting
interfaces for Android, optical
character recognition for Google Drive documents, and recommendation systems
that understand music and YouTube videos. Our approach is driven by algorithms that
benefit from processing very large, partially-labeled datasets using parallel
computing clusters. A good example is our recent work on object recognition using a
novel deep convolutional neural network architecture known as Inception that achieves state-of-the-art results on
academic benchmarks and allows users to easily search through their large
collection of Google Photos. The ability to mine meaningful information from
multimedia is broadly applied throughout Google.